Graph selection with GGMselect

TL;DR

This paper introduces a two-stage graph estimation method for high-dimensional Gaussian graphical models, demonstrating its consistency, risk control, and practical effectiveness on biological gene expression data.

Contribution

It proposes a novel two-stage graph selection procedure with theoretical guarantees and practical implementation for high-dimensional Gaussian graphical models.

Findings

Method is consistent in high-dimensional settings

Risk is controlled by a non-asymptotic oracle inequality

Effective on real gene expression data

Abstract

Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting, and its risk is controlled by a non-asymptotic oracle-like inequality. The procedure is tested on a real data set concerning gene expression data, and its performances are assessed on the basis of a large numerical study. The procedure is implemented in the R-package GGMselect available on the CRAN.